from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-11-30 14:07:12.573944
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Mon, 30, Nov, 2020
Time: 14:07:17
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.0068
Nobs: 126.000 HQIC: -44.2096
Log likelihood: 1317.98 FPE: 2.77876e-20
AIC: -45.0327 Det(Omega_mle): 1.39745e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.588012 0.190102 3.093 0.002
L1.Burgenland 0.141279 0.088558 1.595 0.111
L1.Kärnten -0.308900 0.074309 -4.157 0.000
L1.Niederösterreich 0.044912 0.213158 0.211 0.833
L1.Oberösterreich 0.276946 0.176345 1.570 0.116
L1.Salzburg 0.141879 0.089167 1.591 0.112
L1.Steiermark 0.082644 0.125843 0.657 0.511
L1.Tirol 0.173028 0.083566 2.071 0.038
L1.Vorarlberg 0.025729 0.081148 0.317 0.751
L1.Wien -0.137779 0.168029 -0.820 0.412
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.635393 0.245172 2.592 0.010
L1.Burgenland 0.000270 0.114212 0.002 0.998
L1.Kärnten 0.334405 0.095835 3.489 0.000
L1.Niederösterreich 0.102499 0.274906 0.373 0.709
L1.Oberösterreich -0.239953 0.227430 -1.055 0.291
L1.Salzburg 0.180090 0.114998 1.566 0.117
L1.Steiermark 0.236196 0.162298 1.455 0.146
L1.Tirol 0.135127 0.107773 1.254 0.210
L1.Vorarlberg 0.205925 0.104655 1.968 0.049
L1.Wien -0.563965 0.216704 -2.602 0.009
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.339841 0.082144 4.137 0.000
L1.Burgenland 0.101273 0.038266 2.647 0.008
L1.Kärnten -0.028237 0.032109 -0.879 0.379
L1.Niederösterreich 0.124695 0.092106 1.354 0.176
L1.Oberösterreich 0.272918 0.076200 3.582 0.000
L1.Salzburg -0.012538 0.038530 -0.325 0.745
L1.Steiermark -0.053754 0.054377 -0.989 0.323
L1.Tirol 0.097722 0.036109 2.706 0.007
L1.Vorarlberg 0.143816 0.035064 4.101 0.000
L1.Wien 0.023637 0.072606 0.326 0.745
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.206202 0.096709 2.132 0.033
L1.Burgenland 0.000772 0.045051 0.017 0.986
L1.Kärnten 0.030700 0.037803 0.812 0.417
L1.Niederösterreich 0.074095 0.108438 0.683 0.494
L1.Oberösterreich 0.357009 0.089711 3.980 0.000
L1.Salzburg 0.086491 0.045361 1.907 0.057
L1.Steiermark 0.198753 0.064019 3.105 0.002
L1.Tirol 0.029342 0.042512 0.690 0.490
L1.Vorarlberg 0.112178 0.041282 2.717 0.007
L1.Wien -0.095666 0.085480 -1.119 0.263
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.761761 0.207205 3.676 0.000
L1.Burgenland 0.056770 0.096526 0.588 0.556
L1.Kärnten -0.016270 0.080995 -0.201 0.841
L1.Niederösterreich -0.084791 0.232335 -0.365 0.715
L1.Oberösterreich 0.066268 0.192211 0.345 0.730
L1.Salzburg 0.036542 0.097190 0.376 0.707
L1.Steiermark 0.117334 0.137165 0.855 0.392
L1.Tirol 0.220597 0.091084 2.422 0.015
L1.Vorarlberg 0.034875 0.088449 0.394 0.693
L1.Wien -0.165421 0.183146 -0.903 0.366
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.228574 0.143577 1.592 0.111
L1.Burgenland -0.050880 0.066884 -0.761 0.447
L1.Kärnten -0.016281 0.056123 -0.290 0.772
L1.Niederösterreich 0.177104 0.160990 1.100 0.271
L1.Oberösterreich 0.395843 0.133187 2.972 0.003
L1.Salzburg -0.041245 0.067345 -0.612 0.540
L1.Steiermark -0.055743 0.095044 -0.586 0.558
L1.Tirol 0.202306 0.063114 3.205 0.001
L1.Vorarlberg 0.049077 0.061288 0.801 0.423
L1.Wien 0.124211 0.126906 0.979 0.328
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.284917 0.181721 1.568 0.117
L1.Burgenland 0.072581 0.084654 0.857 0.391
L1.Kärnten -0.083630 0.071033 -1.177 0.239
L1.Niederösterreich -0.126682 0.203760 -0.622 0.534
L1.Oberösterreich -0.106617 0.168571 -0.632 0.527
L1.Salzburg 0.002475 0.085236 0.029 0.977
L1.Steiermark 0.373897 0.120295 3.108 0.002
L1.Tirol 0.537040 0.079882 6.723 0.000
L1.Vorarlberg 0.234956 0.077570 3.029 0.002
L1.Wien -0.180238 0.160621 -1.122 0.262
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.076558 0.209734 0.365 0.715
L1.Burgenland 0.028303 0.097704 0.290 0.772
L1.Kärnten -0.063283 0.081983 -0.772 0.440
L1.Niederösterreich 0.269103 0.235171 1.144 0.253
L1.Oberösterreich 0.022868 0.194557 0.118 0.906
L1.Salzburg 0.231848 0.098376 2.357 0.018
L1.Steiermark 0.162094 0.138839 1.167 0.243
L1.Tirol 0.048653 0.092196 0.528 0.598
L1.Vorarlberg 0.017167 0.089528 0.192 0.848
L1.Wien 0.215922 0.185382 1.165 0.244
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.625225 0.116387 5.372 0.000
L1.Burgenland -0.008820 0.054218 -0.163 0.871
L1.Kärnten -0.004125 0.045494 -0.091 0.928
L1.Niederösterreich -0.061319 0.130502 -0.470 0.638
L1.Oberösterreich 0.274751 0.107964 2.545 0.011
L1.Salzburg 0.000862 0.054591 0.016 0.987
L1.Steiermark 0.011223 0.077045 0.146 0.884
L1.Tirol 0.076650 0.051162 1.498 0.134
L1.Vorarlberg 0.193419 0.049681 3.893 0.000
L1.Wien -0.098562 0.102873 -0.958 0.338
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.095400 -0.049860 0.195120 0.238615 0.008190 0.064698 -0.124863 0.109877
Kärnten 0.095400 1.000000 -0.063079 0.176150 0.079957 -0.170031 0.189472 0.016826 0.266847
Niederösterreich -0.049860 -0.063079 1.000000 0.239301 0.070080 0.162258 0.062506 0.050272 0.356073
Oberösterreich 0.195120 0.176150 0.239301 1.000000 0.239539 0.268187 0.063268 0.055680 0.043281
Salzburg 0.238615 0.079957 0.070080 0.239539 1.000000 0.130660 0.038481 0.087308 -0.066605
Steiermark 0.008190 -0.170031 0.162258 0.268187 0.130660 1.000000 0.087017 0.083915 -0.195077
Tirol 0.064698 0.189472 0.062506 0.063268 0.038481 0.087017 1.000000 0.135484 0.088188
Vorarlberg -0.124863 0.016826 0.050272 0.055680 0.087308 0.083915 0.135484 1.000000 0.080334
Wien 0.109877 0.266847 0.356073 0.043281 -0.066605 -0.195077 0.088188 0.080334 1.000000